Free annotated data for deep learning in microscopy? A hitchhiker's guide

In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical. Can this requirement for annotated data be relaxed? Is it possible to borrow the knowledge gathered from datasets...

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Bibliographic Details
Published inarXiv.org
Main Authors Shajkofci, Adrian, Liebling, Michael
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.10.2020
Subjects
Computer Science - Computer Vision and Pattern Recognition
Datasets
Deep learning
Microscopy
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Summary:In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical. Can this requirement for annotated data be relaxed? Is it possible to borrow the knowledge gathered from datasets in other application fields and leverage it for microscopy? Here, we aim to provide an overview of methods that have recently emerged to successfully train learning-based methods in bio-microscopy.
ISSN:2331-8422
DOI:10.48550/arxiv.2010.03988